Marius Brezovan

A New Method for Segmentation of Images Represented in a HSV Color Space

This paper presents an original low-level system for color image segmentation considering the Hue-Saturation-Value (HSV) color space. Many difficulties of color image segmentation may be resolved using the correct color space in order to increase the effectiveness of color components to discriminate color data. The technique proposed in the article uses new data structures that lead to simpler and more efficient segmentation algorithms. We introduce a flexible hexagonal network structure on the pixels image and we extract for each segmented region the syntactic features that can be used in the shape recognition process. Our technique has a time complexity lower than the methods studied from specialized literature and the experimental results on Berkeley Segmentation Dataset color image database show that the performance of method is robust.

Automatic image annotation and semantic based image retrieval for medical domain

Automatic image annotation is the process of assigning meaningful words to an image taking into account its content. This process is of great interest as it allows indexing, retrieving, and understanding of large collections of image data. This paper presents a system used in the medical domain for three distinct tasks: image annotation, semantic based image retrieval and content based image retrieval. An original image segmentation algorithm based on a hexagonal structure was used to perform the segmentation of medical images. Images regions are described using a vocabulary of blobs generated from image features using the K-means clustering algorithm. The annotation and semantic based retrieval task is evaluated for two annotation models: Cross Media Relevance Model and Continuous-space Relevance Model. Semantic based image retrieval is performed using the methods provided by the annotation models. The ontology used by the annotation process was created in an original manner starting from the information content provided by the Medical Subject Headings (MeSH). The experiments were made using a database containing color images retrieved from medical domain using an endoscope and related to digestive diseases.

An Adaptive Method for Efficient Detection of Salient Visual Object from Color Images

This paper presents an efficient graph-based method to detect salient objects from color images and to extract their color and geometric features. Despite of the majority of the segmentation methods our method is totally adaptive and it do not require any parameter to be chosen in order to produce a better segmentation. The proposed segmentation method uses a hexagonal structure defined on the set of the image pixels ant it performs two different steps: a pre-segmentation step that will produce a maximum spanning tree of the connected components of the visual graph constructed on the hexagonal structure of an image, and the final segmentation step that will produce a minimum spanning tree of the connected components, representing the visual objects, by using dynamic weights based on the geometric features of the regions. Experimental results are presented indicating a good performance of our method.

Computational Complexity Analysis of the Graph Extraction Algorithm for 3D Segmentation

The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. Unfortunately there are huge of papers for 2D images and segmentation methods and most graph-based for 2D images and few papers for spatial segmentation methods. We attempt to search a certain structures in the associated edge weighted spatial graph constructed on the image voxels, such as minimum spanning tree. The major concept used in graph-based 3D clustering algorithms is the concept of homogeneity of regions. For color 3D segmentation algorithms the homogeneity of regions is color-based, and thus the edge weights are based on color distance. Early graph-based methods use fixed thresholds and local measures in finding a 3D segmentation. Complex grouping phenomena can emerge from simple computation on these local cues. A number of approaches to segmentation are based on finding compact clusters in some feature space. A recent technique using feature space clustering first transforms the data by smoothing it in a way that preserves boundaries between regions. Our previous works are related to other works in the sense of pair-wise comparison of region similarity. In this paper we extend our previous work by adding a new step in the spatial segmentation algorithm that allows us to determine regions closer to it. We use different measures for internal contrast of a connected component and for external contrast between two connected components than the measures. The key to the whole algorithm of spatial segmentation is the honeycomb. The preprocessing module is used mainly to blur the initial RGB spatial image in order to reduce the image noise by applying a 3D Gaussian kernel. Then the segmentation module creates virtual cells of prisms with tree-hexagonal structure defined on the set of the image voxels of the input spatial image and a spatial triangular grid graph having tree-hexagons as cells of vertices.

A comparative study of some methods for color medical images segmentation

The aim of this article is to study the problem of color medical images segmentation. The images represent pathologies of the digestive tract such as ulcer, polyps, esophagites, colitis, or ulcerous tumors, gathered with the help of an endoscope. This article presents the results of an objective and quantitative study of three segmentation algorithms. Two of them are well known: the color set back-projection algorithm and the local variation algorithm. The third method chosen is our original visual feature-based algorithm. It uses a graph constructed on a hexagonal structure containing half of the image pixels in order to determine a forest of maximum spanning trees for connected component representing visual objects. This third method is a superior one taking into consideration the obtained results and temporal complexity. These three methods were successfully used in generic color images segmentation. In order to evaluate these segmentation algorithms, we used error measuring methods that quantify the consistency between them. These measures allow a principled comparison between segmentation results on different images, with differing numbers of regions generated by different algorithms with different parameters.

Evaluation of image segmentation algorithms from the perspective of salient region detection

The present paper addresses the problem of image segmentation evaluation by comparing seven different approaches. We are presenting a new method of salient object detection with very good results relative to other already known object detection methods. We developed a simple evaluation framework in order to compare the results of our method with other segmentation methods. The results of our experimental work offer good perspectives for our algorithm, in terms of efficiency and precision.

New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure

This paper presents a method for detection of salient objects from images. The proposed algorithms for image segmentation and objects detection use a hexagonal representation of the im ...

Database Kernel for Image Retrieval

This article presents a software tool that implements a dedicated multimedia database management server for managing alphanumerical and multimedia data collections from medical domain. An element of originality for this database management system (DBMS) is that along with classical operations for databases, it includes a series of algorithms used for extracting visual information from images (texture and color characteristics). The color histogram with 166 colors in HSV space represents the image color information. A vector with 12 values represents the texture information obtained by applying Gabor filters. The extracted data are stored in the database in a special data type called IMAGE, with a specific structure that can be used for visual queries. To increase the image retrieval speed, there are used some clustering algorithms.

New algorithm for segmentation of images represented as hypergraph hexagonal-grid

This paper presents a new method for segmentation of images into regions and for boundary extraction that reflect objects present in the image scene. The unified framework for image processing uses a grid structure defined on the set of pixels from an image. We propose a segmentation algorithm based on hypergraph structure which produces a maximum spanning tree of a visual hypergraph constructed on the grid structure, and we consider the HCL (Hue-Chroma-Luminance) color space representation. Our technique has a time complexity lower than the methods from the specialized literature, and the experimental results on the Berkeley color image database show that the performance of the method is robust.

An hypergraph object oriented model for image segmentation and annotation

This paper presents a system for segmentation of images into regions and annotation of these regions for semantic identification of the objects present in the image. The unified method for image segmentation and image annotation uses an hypergraph model constructed on the hexagonal structure. The hypergraph structure is used for representing the initial image, the results of segmentation processus and the annotation information together with the RDF ontology format. Our technique has a time complexity much lower than the methods studied in the specialized literature, and the experimental results on the Berkeley Dataset show that the performance of the method is robust.